EP1231475B1 - Procedure and method of determining the state of a technical system such as an energy storage - Google Patents

Procedure and method of determining the state of a technical system such as an energy storage Download PDF

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Publication number
EP1231475B1
EP1231475B1 EP01126904A EP01126904A EP1231475B1 EP 1231475 B1 EP1231475 B1 EP 1231475B1 EP 01126904 A EP01126904 A EP 01126904A EP 01126904 A EP01126904 A EP 01126904A EP 1231475 B1 EP1231475 B1 EP 1231475B1
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EP
European Patent Office
Prior art keywords
state
variables
parameters
estimation
means
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Expired - Fee Related
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EP01126904A
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German (de)
French (fr)
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EP1231475A3 (en
EP1231475A2 (en
Inventor
Christel Sarfert
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Robert Bosch GmbH
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Robert Bosch GmbH
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Publication of EP1231475A3 publication Critical patent/EP1231475A3/en
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367Software therefor, e.g. for battery testing using modelling or look-up tables

Description

    Field of the invention
  • The invention relates to a method for detecting the state of a technical system, in particular an energy store, in which operating variables are measured and supplied to a state estimation routine which determines state variables that characterize the current system state by means of a model based on system dependent model parameters and the measured operating variables measured parameters and possibly the particular state variables can be additionally supplied to a parameter estimation routine, which in turn determines the model parameters depending on the use by estimation. The invention further relates to a corresponding device and a computer program for carrying out the method as well as a computer program product.
  • State of the art
  • From the German patent application DE-199 59 019.2 a generic method for battery state detection is described. Starting from measurable operating variables, such as Current, voltage and temperature, there are state variables by means of a model, which is implemented in the form of a (Kalman) filter, determined by estimation. Since the parameters of the model change as a result of the aging of the battery as well as due to possible sudden defects, a parameter estimation routine is additionally provided for tracking these parameter changes online and for adapting the parameters accordingly. The current parameters are then fed to the state estimation routine, the filter. This ensures that the model is constantly adapted to the actual state of the battery and the filter does not estimate incorrect values for the state variables. The separation of the estimation of state variables and parameters by the filter or the parameter estimator causes biased estimates to be avoided or unlikely.
  • It has been found that the described method for state recognition by estimating both the state variables and the underlying model parameters is often insufficient to guarantee a required accuracy of the estimated values and to avoid divergences in the covariance matrices frequently used for the estimation.
  • It is therefore desirable to make the condition and parameter estimation more stable and to make possible such an estimate for all conceivable system states with the lowest possible computation and memory requirements.
  • From the document "Technical Report Ucla-csd 010001," Dynamic Data Factorization, Soatto, S., Chiuso, Alessandro, page 1-7 "methods for detecting the state of a technical system are known in which operating variables are measured and fed to a state estimation routine. In this case, current system states and characterizing state variables are determined by means of a model based on system-dependent model parameters and the measured operating variables, wherein the measured operating variables and possibly the determined state variables are additionally fed to a parameter estimation routine for improving the state estimation. This in turn determines the model parameters depending on use, taking into account a possibly existing dynamics of the measured operating variables. The described methods run, for example, in computer programs, which may also comprise program code means.
  • The document "Systems & Control Letters 31 (20.11.1997)," Convergent Algorithms for Frequency Weighted L2 Module Reduction ", Yan, W.-Y., Xie, L., Lam, J" shows methods for detecting the state of a technical system are measured to the farm sizes. State variables are taken into account in the state estimation. A selection of state variables to be determined by estimation can take place as a function of the dynamics of the measured operating variables. Both of the aforementioned publications relate generally to mathematical model calculations.
  • From the paper "Signal Processing and Systems Control, Intelligent Sensors and Instrumentation, San Diego, Nov. 9-13, 1992, Proceedings of the International Conference on Industrial Electronics, Control, Instrumentation and Automation (iecon), New York, Ieee, Us (09-11-1992), 3 COND. 18, 996-1001 "a model-based battery condition detection is known that operates using a computer.
  • Advantages of the invention
  • In the method according to the invention, only certain state variables and / or parameters are used for the estimation, this selection is based on the dynamics of the measured operating variables. A device according to the invention thus has means for determining the dynamics of the measured operating variables, for example means for forming the temporal gradient of the respective operating variable, as well as selection means by which certain state variables and / or parameters are determined by the corresponding estimating routines. Such selection means can be implemented, for example, in the form of tables, step functions or threshold value functions, by means of which specific parameters or state variables are assigned to specific dynamic ranges of the operating variables.
  • For state estimation, so-called Kalman filters are often used which work with covariance matrices of the estimated quantities. Covariance matrices represent on their diagonal the mean square deviation of the estimated from the measured value, the remaining matrix elements represent the correlations between the individual state variables. The method according to the invention reduces the order of these matrices and thus reduces the numerical complexity and the required memory requirement. Also, it is now easier to determine those parameters which occur at different times and at different excitations, i. at the existing farm sizes, change.
  • With high dynamics of the measured operating variables, it is advantageous to estimate those state variables or parameters which have small time constants and to use those with a large time constant with low dynamics of the measured operating variables for the estimation. The respective other state variables or parameters become meanwhile held or tracked by means of a predetermined model.
  • In the battery state detection example, a battery model uses different resistance and voltage quantities with different time constants. Ohmic values and the so-called pass-over overvoltage have small time constants and are preferably estimated when the measured operating variables have great dynamics. By contrast, for example, the concentration overvoltage has a large time constant, so this size can be estimated with low dynamics. The other quantities are to be recorded during the estimation or changed according to a given pattern.
  • It is also advantageous if it is determined before the estimation determination whether the system is in a limit state and if the state variables or parameters are estimated only when the system is not in such a limit state. Such limit states are present, for example, at the beginning and at the end of the life of a technical system. In the example of the battery state detection, this means that in a situation in which the battery is almost fully charged, more accurate estimates can be dispensed with, since this limit state is a desired and non-critical area. Another case is when the battery gets into a very bad (charging) state. Since this is usually detected sufficiently early and thus a complete failure of the battery can be avoided, this limit state is also not of particular relevance.
  • By hiding the estimate in the limits of model accuracy, divergences of the filter / estimator become and poor qualities of the determined sizes avoided. If, for example, the state of charge of a fully charged battery deteriorates again, the battery automatically enters an operating point in which the model used has full validity and a Kalman filter can supply high-quality estimates. There are also advantages in terms of the required hardware. This results in lower numerical complexity, thus a lower utilization of the processor and lower demands on the memory requirements in the RAM.
  • In the method according to the invention, it is furthermore useful to control the quality of the estimation determination on the basis of an already mentioned covariance matrix. Namely, the smaller the value in this covariance matrix for the respective state variable, the more likely or more accurate the estimated value of that quantity. The same applies to the parameter estimation. Here, too, conventional covariance matrices (eg Bayesian, maximum likelihood method) are used in the popular estimation theories, which make statements about the quality of the parameter estimation and / or the accuracy of the estimated parameters. The procedure here is then approximately the same as with the state estimator. The convergence (to values near zero) of the matrix sizes associated with the estimates may be used to evaluate the quality of the estimate. In addition, by appropriate weighting of the determined results, the overall statement regarding the state variables, such as the state of charge and aging of the battery, can be increased even further.
  • A simple way of determining the quality of the estimate is to establish a threshold value for the matrix value assigned to the respective estimated variable. These thresholds are determined by empirical values and are usually close to zero.
  • If the estimated values are only of low quality, other so-called "back-up" methods can be included in the evaluation with greater weighting. By means of such "back-up" procedures, the respective quantities are recorded or adjusted according to simple models that do not lead to divergences. On the other hand, it may be decided that, for example, certain parameters are not currently adopted by the state estimation routine, or certain states may not instantaneously initiate the parameter estimation routine.
  • The invention is explained below with reference to an illustrated by the accompanying drawings embodiment.
  • Fig. 1 shows the schematic arrangement of the components of a device according to the invention for state detection of an energy storage device.
  • FIG. 2 shows a schematic example of the convergence of a state variable and the associated covariance.
  • 3 schematically shows an example of the divergence of an estimated state variable and the associated covariance.
  • 4 shows a flowchart of an exemplary embodiment of the method according to the invention.
  • Fig. 5 shows a flow chart for determining the quality of the estimated quantities.
  • The present embodiment can be read for state detection of energy storage devices such as car batteries, but is not limited thereto.
  • In Fig. 1, the components for state detection according to the invention of an energy storage device 1, such as a car battery, are shown schematically. A sensor and measuring unit 2 makes measurements of operating variables x, such as current, voltage and / or temperature, at the battery 1. The measured operating variables are supplied via lines 7 to a state estimator 3, which determines, for example by means of a Kalman filter in a known manner, state variables which characterize the current system state. Such state quantities a may be the available charge or the age of the battery 1. To determine these state variables a, the state estimator 3 uses a model in which the measured operating variables x are input. The model itself works with model parameters p, which are also dependent on aging processes of the energy store 1. In order to avoid that the model loses its validity due to changed parameters p, the model parameters p are updated by means of a parameter estimator 4. For this purpose, a parameter estimation routine is used which uses the measured operating variables x and possibly additionally the estimated state variables a as input variables. The updated parameters p are then passed to the state estimator 3. For this purpose, state estimator 3 and parameter estimator 4 are interconnected.
  • The state variables a determined by the state estimator 3 are processed further in order to take respectively favorable measures (for example charge state display, modification of the energy supply).
  • 1B shows a construction which is as appropriate for the state treasures 3 as well as for the parameter estimator 4, in which the individual components for state detection according to the invention are combined in one unit. The measured operating variables x are supplied to the state estimator 3 or the parameter estimator 4 via lines 7. As a means 8 for detecting the dynamics of the measured operating variables x difference or differentiators are provided, each forming the gradient of a measured variable x. Downstream is a selection unit 9, which depending on the detected dynamics of these operating variables x selects those state variables a or parameter p, which are to be estimated below. The selected operating variables x are supplied to the state estimator 3 together with the updated parameters p to a calculation unit 10 which calculates certain state variables a by means of a model. Most estimation models work with so-called covariance matrices whose values associated with individual state variables converge to zero as the estimated value approaches time in time. These matrix values (covariances) can therefore be used to assess the quality of the estimate.
  • In order to assess the quality of the estimation, threshold values corresponding to the respective covariances are defined in a unit 11 and the quality of the estimate is determined by forming the difference between the estimated value and the defined threshold value. For example, if an estimated state quantity does not fall below this threshold after a given number of cycles, then the estimated value may be discarded and instead the previously estimated value maintained. In this way you prevent an increasing deterioration of the estimate.
  • Further details and further possibilities of the invention will become apparent from the following figures.
  • Fig. 2A shows an example of a rapidly converging estimated state quantity a (3) which is not subject to variations after convergence. Such state variables, such as the concentration overvoltage, have large time constants. The associated matrix element of the covariance matrix shown in FIG. 2B, here K (3,3) to a (3), that is to say the so-called covariance to this state variable, rapidly converges to zero. To check the quality of the estimate, a threshold value can be set, which should be reached after a certain number of cycles, that is, number of iterative estimates. If this is not the case, the estimate for this state variable can be discarded.
  • An example for a divergence of a current state quantity ã (1) and the associated estimated value a (1) is shown in FIG. 3, wherein in FIG. 3A the time course of the current state variable ã (1) oscillating below the zero line and the time from the zero-line removing estimated state value a (1) are shown. That this estimate is inappropriate reflects the associated covariance, K (1,1), to the state quantity a (1). The covariance does not converge, but increases steadily in time, as shown in Fig. 3B.
  • The invention avoids cases, as shown in FIG. 3, by resorting to so-called "back-up" methods when the quality of the estimate is insufficient.
  • 4 shows the flow chart of an example for carrying out the method according to the invention. At the beginning of Estimation process is first waited for a certain time T min1 , until the system has assumed a condition suitable for state estimation, before the actual estimation process begins. Subsequently, the dynamics of the excitation, ie the dynamics of the measured operating quantities x are queried (S1). These are, for example, the time-dependent quantities current, temperature and voltage. If, for example, the discharge current of the battery remains almost zero over a longer period of time, since, for example, the consumers are completely supplied by the generator, then it is not to be expected that certain current-dependent state variables a or parameter p will undergo a change. Then further measured values are awaited until a further time interval T min2 has elapsed (S2).
  • If a dynamic of the measured operating variables is used, the size of this dynamic is queried (S3). At low dynamics of the measured values, it is first determined whether the system is in a limit state or edge region (in the case of batteries, for example, the fully charged or the deflated state). The same query also occurs in the case of a large dynamic of the measured operating variables (S4 or S5).
  • If the system is not in an edge area, the actual estimation of the state variables can be started. According to the invention, the state variables with a small time constant are recorded (S6) while the state variables with a high time constant are estimated (S6) with low dynamics of the measured values. Conversely, in the case of measured values with great dynamics, the state variables are recorded with a large time constant (S8), while the state variables with a small time constant are estimated (S9). In the battery model discussed here, as already mentioned, On the other hand, those parameters and state variables are not newly determined by estimation, of which no changes are to be expected due to the dynamics of the system. This avoids unnecessarily frequent estimations that increase inaccuracies in the estimation, which then falsify the model or provide false condition results.
  • If it is determined that this is the case when checking whether the system is in a limit state (edge region) (S4, S5), the state variables / parameters are recorded or, for example, avoided by means of "misjudgments" (limits of model accuracy). Back-up "method evaluated (S10, S11). Such methods are based on stable models in which no divergence is to be expected.
  • After the end of the routine illustrated in FIG. 4, one cycle of the state estimation according to the invention is ended, and further cycles may follow immediately or with delays to be established.
  • Fig. 5 shows a flow chart for determining the quality of the estimate described above. For this purpose, an easily measurable variable, which is calculated from estimated quantities, is first compared with the actually measured quantity (T1). With good agreement (for example, the estimated and the measured battery voltage), the convergence of the covariances belonging to the state variables / parameters is checked (T2). It is possible that individual covariances are not yet sufficiently converged (see also Fig. 2B), so that in this case a certain typical convergence time T min must be awaited (T3) until sufficiently good convergence results. If this is the case, the estimated state variables / parameters are evaluated (T4) and from this, for example, the state of charge or the age of the battery is determined.
  • If, on the other hand, the time T min has already elapsed without the associated covariances having sufficiently converged, ie, for example, falling below a certain threshold value, the estimated quantities are discarded and the parameter estimation routine and / or the state estimation routine (Kalman filter) are restarted (T5). Until the re-estimated sizes are obtained, simple back-up procedures are used (T6).
  • If there is no sufficient agreement between an easily measurable and estimated reference variable (for example battery voltage) from the outset, then the covariance matrix can not be sufficiently converged. This result can be checked again after a period T min * (T7). If this result remains unchanged, the parameter and / or state estimation is also restarted (see FIG. 4) and recourse to back-up methods (T8, T9).

Claims (8)

  1. Method for detecting the state of a technical system (1), in which operating variables (x) are measured and supplied to a state estimation routine which determines state variables (a), which characterize the current system state, by means of a model which is based on system-dependent model parameters (p) and the measured operating variables (x), with the measured operating variables (x) and possibly the determined state variables (a) additionally being supplied to a parameter estimation routine in order to improve the state estimation, which parameter estimation routine for its part determines the model parameters (p) in a use-dependent manner, and with state variables (a) and/or parameters (p) which are to be determined by estimation being selected as a function of the dynamics of the measured operating variables (x), characterized in that the technical system (1) is an electrical energy store, in that the state variables (a) and/or parameters (p) which are not selected are retained in unchanged form or are reset using firmly prescribed models, and in that state variables (a) and/or parameters (p) with a small time constant are selected for estimation in the case of high dynamics of the measured operating variables (x), and state variables and/or parameters with a large time constant are selected for estimation in the case of low dynamics.
  2. Method according to Claim 1, characterized in that it is established whether the system (1) is in a limit state, in particular at the start or at the end of its service life, before the estimation, and in that the state variables (a) and/or parameters (p) are not selected for estimation when the system (1) is in such a limit state.
  3. Method according to Claim 1 or 2, characterized in that the quality of the estimation is monitored using a covariance matrix.
  4. Method according to Claim 3, characterized in that the estimated state variables (a) and/or parameters (p) are only used further when their associated covariances of the covariance matrix converge.
  5. Apparatus for detecting the state of a technical system (1), having means (2) for measuring the operating variables (x) of this system (1), and having means (7) for supplying the measured operating variables (x) to a state estimator (3) which determines state variables (a), which characterize the current system state, by means of a model which is based on system-dependent model parameters (p) and the measured operating variables (x), it being possible for a parameter estimator (4) to be additionally provided in order to improve the state estimation, which parameter estimator can be supplied with the measured operating variables (x) and the determined state variables (a) and determines the model parameters in a use-dependent manner, with means (8) for detecting the dynamics of the measured operating variables (x) and an associated selector unit (9) being provided, characterized in that the technical system is an energy store, in that the selector unit (9) is designed to select the state variables (a) and/or parameters (p) to be determined in the state estimator and/or parameter estimator (4) as a function of the detected dynamics, and in that means are provided which select state variables (a) and/or parameters (p) with small time constants for estimation in the case of high dynamics of the measured operating variables (x), and select state variables and/or parameters with a large time constant for estimation in the case of low dynamics, and which retain the state variables (a) and/or parameters (p) which are not selected in unchanged form or reset said state variables and/or parameters using firmly prescribed models.
  6. Apparatus according to Claim 5, characterized in that means (10) for calculating a covariance matrix for the state variables (a) and/or parameters (p) to be determined and means (11) for evaluating the resulting covariance matrix are provided.
  7. Computer program with program-code means for carrying out all of the steps of at least one of Claims 1 to 4 when the computer program is run on a computer or a corresponding computer unit, in particular on the state estimator (3) and/or parameter estimator (4).
  8. Computer-program product with program-code means, which are stored on a computer-readable data-storage medium, for carrying out a method according to one of Claims 1 to 4 when the computer-program product is run on a computer or a corresponding computer unit, in particular the state estimator (3) and/or the parameter estimator (4).
EP01126904A 2001-02-13 2001-11-13 Procedure and method of determining the state of a technical system such as an energy storage Expired - Fee Related EP1231475B1 (en)

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